Country Statistical Focus

Intro

Country Focus: 6 key questions for each country addressed through predefined static charts

Charts can easily be copied as an image, or adjusted (for instance changing the title to support a data story) and embedded in reports, fact-sheets or presentations to accompany a narrative.

The charts includes the 6 following topics:

1. Evolution over time of the different Categories of Forcibly Displaced People?

2. Evolution of the different Country of Origin of population in need of international protection

3. What is the demographics profile of population in need of international protection in the country in relation with the host population?

4. What are the trends in term of solutions in this Country?

5. What is the asylum processing capacity in relation with the demand?

6. if relevant, evolution of the Country seen as a displacement source country?

America

Argentina

Categories

Origin

#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
data <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long, 
                                                     y= unhcrdatapackage::reference, 
                                                     by = c("CountryAsylumCode" = "iso_3"))  %>%
  filter(CountryAsylumName  == thiscountry & Year == lastyear) %>%   
  mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
        CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
        CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>% 
  group_by( CountryOriginName, SUBREGION) %>%
  summarise(Value2 = sum(Value) )  %>%
  #mutate( value3 =  format_si(Value2)) %>%
  arrange(desc(Value2)) %>%
  head(10)

#Make plot
ggplot(data, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
                           y = Value2)) +
  geom_bar(stat = "identity", 
           position = "identity", 
           fill = "#0072bc") + # here we configure that it will be bar chart+
  geom_label(aes(x = CountryOriginName, y = Value2, 
                label =  format(round(Value2, -3),  big.mark=",")), 
             hjust = 1, 
             vjust = 0.5, 
             colour = "white", 
             fill = NA, 
             label.size = NA, 
             family = "Lato", 
             size = 6) +
  geom_hline(yintercept = 0, size = 1, colour = "#333333") +
  coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
  ## and the chart labels
  labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
       subtitle = paste0("Data as of ",lastyear), 
       x = " ",
       y = "# of Forcibly displaced people",
       caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
  scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
  geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
  unhcRstyle::unhcr_theme(base_size = 8)  + ## Insert UNHCR Style
  theme(panel.grid.major.x = element_line(color = "#cbcbcb"), 
        panel.grid.major.y = element_blank()) ### changing grid line that should appear

Demographics

demographics1 <- demographics %>%
                   dplyr::left_join( unhcrdatapackage::reference %>% 
                                       select(UNHCRBureau, iso_3),  
                                     by = c("CountryAsylumCode" = "iso_3")) %>% 
  filter(CountryAsylumName  == thiscountry & Year == lastyear-1) %>%
                  mutate ( totGen = FemaleTotal +MaleTotal,
                           totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
                             Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
                           hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))

tot <- format( sum(demographics1$Total) ,  big.mark=",")
totprop <-   format( round( sum(demographics1$totGen) / 
    sum(demographics1$Total )  *100,1),  big.mark=",")  

if (totprop == 0 ) {
  cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
  
} else {
#names(demographics)
pyramid <-  demographics1[ demographics1$Year == max(demographics1$Year),
                          c(
                              "Female04",
                              "Female511",
                              "Female1217",
                              "Female1859",
                              "Female60ormore",
                              "FemaleUnknown",
                             # "FemaleTotal",
                              "Male04",
                              "Male511",
                              "Male1217",
                              "Male1859",
                              "Male60ormore",
                              "MaleUnknown"#,
                             # "MaleTotal"       
                             )]  

pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
  pivot_longer(
    cols = Female04:MaleUnknown,
    names_to = "Class",
    values_to = "Sum",
    values_drop_na = TRUE
  ) 

pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
                                          by = list(pyramid2$Class#, pyramid2$REGION_UN
                                                    ),
                                          sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"

pyramid3 <- pyramid3 %>% 
  mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
                            str_detect(Class, "Female") ~ "Female")) %>% 
  mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
                         str_detect(Class, "511") ~ "5-11",
                         str_detect(Class, "1217") ~ "12-17",
                         str_detect(Class, "1859") ~ "18-59",
                         str_detect(Class, "60") ~ "60+",
                         str_detect(Class, "Unknown") ~ "Unknown")) 

pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11",  "12-17",  "18-59", "60+", "Unknown"))

pyramidplot <- ggplot(pyramid3, aes(x = age, 
                     fill = gender,
                      y = ifelse(test = gender == "Female",
                            yes = -pc, no = pc)) ) + 
  geom_bar(stat = "identity") +
  geom_label(aes(x = age,
                 y = ifelse(test = gender == "Female",
                            yes = -pc -5 , no = pc),
                label =  format(round(pc, 1),  big.mark=",") 
                ),
             hjust = 0,
             vjust = 0.5,
             colour = "black",
             fill = NA,
             label.size = NA,
             family = "Lato",
             size = 8) +
  scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
  labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
       subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
       x = "", 
       y = "Percent of population",
       caption =  "Source: UNHCR Population Statistics as of end 2020") +
  scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
                      aesthetics = c("colour", "fill")) +
  coord_flip()+
  unhcRstyle::unhcr_theme(base_size = 8) +
   theme(panel.grid.major.x = element_line(color = "#cbcbcb"), 
            panel.grid.major.y = element_blank())

pyramidplot
}

Solution

Processing

RSD activities are mostly for ..

Source

#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
data <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long, 
                                                     y= unhcrdatapackage::reference, 
                                                     by = c("CountryAsylumCode" = "iso_3"))  %>%
  filter(CountryOriginName  == thiscountry & Year == lastyear) %>%   
  mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
        CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
        CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>% 
  group_by( CountryAsylumName, SUBREGION) %>%
  summarise(Value2 = sum(Value) )  %>%
  #mutate( value3 =  format_si(Value2)) %>%
  arrange(desc(Value2)) %>%
  head(10)

#Make plot
ggplot(data, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
                           y = Value2)) +
  geom_bar(stat = "identity", 
           position = "identity", 
           fill = "#0072bc") + # here we configure that it will be bar chart+
  geom_label(aes(x = CountryAsylumName, y = Value2, 
                label =  format(round(Value2, -3),  big.mark=",")), 
             hjust = 1, 
             vjust = 0.5, 
             colour = "white", 
             fill = NA, 
             label.size = NA, 
             family = "Lato", 
             size = 6) +
  geom_hline(yintercept = 0, size = 1, colour = "#333333") +
  coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
  ## and the chart labels
  labs(title = paste0("Top 10 Countries hosting Persons from ",thiscountry, " in needs of International Protection" ),
       subtitle = paste0("Data as of ",lastyear), 
       x = "",
       y = "",
       caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
  scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
  geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
  unhcRstyle::unhcr_theme(base_size = 8)  + ## Insert UNHCR Style
  theme(panel.grid.major.x = element_line(color = "#cbcbcb"), 
        panel.grid.major.y = element_blank()) ### changing grid line that should appear

Brazil

Categories

Origin

#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
data <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long, 
                                                     y= unhcrdatapackage::reference, 
                                                     by = c("CountryAsylumCode" = "iso_3"))  %>%
  filter(CountryAsylumName  == thiscountry & Year == lastyear) %>%   
  mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
        CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
        CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>% 
  group_by( CountryOriginName, SUBREGION) %>%
  summarise(Value2 = sum(Value) )  %>%
  #mutate( value3 =  format_si(Value2)) %>%
  arrange(desc(Value2)) %>%
  head(10)

#Make plot
ggplot(data, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
                           y = Value2)) +
  geom_bar(stat = "identity", 
           position = "identity", 
           fill = "#0072bc") + # here we configure that it will be bar chart+
  geom_label(aes(x = CountryOriginName, y = Value2, 
                label =  format(round(Value2, -3),  big.mark=",")), 
             hjust = 1, 
             vjust = 0.5, 
             colour = "white", 
             fill = NA, 
             label.size = NA, 
             family = "Lato", 
             size = 6) +
  geom_hline(yintercept = 0, size = 1, colour = "#333333") +
  coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
  ## and the chart labels
  labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
       subtitle = paste0("Data as of ",lastyear), 
       x = " ",
       y = "# of Forcibly displaced people",
       caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
  scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
  geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
  unhcRstyle::unhcr_theme(base_size = 8)  + ## Insert UNHCR Style
  theme(panel.grid.major.x = element_line(color = "#cbcbcb"), 
        panel.grid.major.y = element_blank()) ### changing grid line that should appear

Demographics

demographics1 <- demographics %>%
                   dplyr::left_join( unhcrdatapackage::reference %>% 
                                       select(UNHCRBureau, iso_3),  
                                     by = c("CountryAsylumCode" = "iso_3")) %>% 
  filter(CountryAsylumName  == thiscountry & Year == lastyear-1) %>%
                  mutate ( totGen = FemaleTotal +MaleTotal,
                           totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
                             Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
                           hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))

tot <- format( sum(demographics1$Total) ,  big.mark=",")
totprop <-   format( round( sum(demographics1$totGen) / 
    sum(demographics1$Total )  *100,1),  big.mark=",")  

if (totprop == 0 ) {
  cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
  
} else {
#names(demographics)
pyramid <-  demographics1[ demographics1$Year == max(demographics1$Year),
                          c(
                              "Female04",
                              "Female511",
                              "Female1217",
                              "Female1859",
                              "Female60ormore",
                              "FemaleUnknown",
                             # "FemaleTotal",
                              "Male04",
                              "Male511",
                              "Male1217",
                              "Male1859",
                              "Male60ormore",
                              "MaleUnknown"#,
                             # "MaleTotal"       
                             )]  

pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
  pivot_longer(
    cols = Female04:MaleUnknown,
    names_to = "Class",
    values_to = "Sum",
    values_drop_na = TRUE
  ) 

pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
                                          by = list(pyramid2$Class#, pyramid2$REGION_UN
                                                    ),
                                          sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"

pyramid3 <- pyramid3 %>% 
  mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
                            str_detect(Class, "Female") ~ "Female")) %>% 
  mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
                         str_detect(Class, "511") ~ "5-11",
                         str_detect(Class, "1217") ~ "12-17",
                         str_detect(Class, "1859") ~ "18-59",
                         str_detect(Class, "60") ~ "60+",
                         str_detect(Class, "Unknown") ~ "Unknown")) 

pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11",  "12-17",  "18-59", "60+", "Unknown"))

pyramidplot <- ggplot(pyramid3, aes(x = age, 
                     fill = gender,
                      y = ifelse(test = gender == "Female",
                            yes = -pc, no = pc)) ) + 
  geom_bar(stat = "identity") +
  geom_label(aes(x = age,
                 y = ifelse(test = gender == "Female",
                            yes = -pc -5 , no = pc),
                label =  format(round(pc, 1),  big.mark=",") 
                ),
             hjust = 0,
             vjust = 0.5,
             colour = "black",
             fill = NA,
             label.size = NA,
             family = "Lato",
             size = 8) +
  scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
  labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
       subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
       x = "", 
       y = "Percent of population",
       caption =  "Source: UNHCR Population Statistics as of end 2020") +
  scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
                      aesthetics = c("colour", "fill")) +
  coord_flip()+
  unhcRstyle::unhcr_theme(base_size = 8) +
   theme(panel.grid.major.x = element_line(color = "#cbcbcb"), 
            panel.grid.major.y = element_blank())

pyramidplot
}

#>  Gender disaggregation is not available for all of the 425,521 persons inBrazil

Solution

Processing

RSD activities are mostly for ..

Source

#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
data <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long, 
                                                     y= unhcrdatapackage::reference, 
                                                     by = c("CountryAsylumCode" = "iso_3"))  %>%
  filter(CountryOriginName  == thiscountry & Year == lastyear) %>%   
  mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
        CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
        CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>% 
  group_by( CountryAsylumName, SUBREGION) %>%
  summarise(Value2 = sum(Value) )  %>%
  #mutate( value3 =  format_si(Value2)) %>%
  arrange(desc(Value2)) %>%
  head(10)

#Make plot
ggplot(data, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
                           y = Value2)) +
  geom_bar(stat = "identity", 
           position = "identity", 
           fill = "#0072bc") + # here we configure that it will be bar chart+
  geom_label(aes(x = CountryAsylumName, y = Value2, 
                label =  format(round(Value2, -3),  big.mark=",")), 
             hjust = 1, 
             vjust = 0.5, 
             colour = "white", 
             fill = NA, 
             label.size = NA, 
             family = "Lato", 
             size = 6) +
  geom_hline(yintercept = 0, size = 1, colour = "#333333") +
  coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
  ## and the chart labels
  labs(title = paste0("Top 10 Countries hosting Persons from ",thiscountry, " in needs of International Protection" ),
       subtitle = paste0("Data as of ",lastyear), 
       x = "",
       y = "",
       caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
  scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
  geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
  unhcRstyle::unhcr_theme(base_size = 8)  + ## Insert UNHCR Style
  theme(panel.grid.major.x = element_line(color = "#cbcbcb"), 
        panel.grid.major.y = element_blank()) ### changing grid line that should appear

Canada

Categories

Origin

#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
data <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long, 
                                                     y= unhcrdatapackage::reference, 
                                                     by = c("CountryAsylumCode" = "iso_3"))  %>%
  filter(CountryAsylumName  == thiscountry & Year == lastyear) %>%   
  mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
        CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
        CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>% 
  group_by( CountryOriginName, SUBREGION) %>%
  summarise(Value2 = sum(Value) )  %>%
  #mutate( value3 =  format_si(Value2)) %>%
  arrange(desc(Value2)) %>%
  head(10)

#Make plot
ggplot(data, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
                           y = Value2)) +
  geom_bar(stat = "identity", 
           position = "identity", 
           fill = "#0072bc") + # here we configure that it will be bar chart+
  geom_label(aes(x = CountryOriginName, y = Value2, 
                label =  format(round(Value2, -3),  big.mark=",")), 
             hjust = 1, 
             vjust = 0.5, 
             colour = "white", 
             fill = NA, 
             label.size = NA, 
             family = "Lato", 
             size = 6) +
  geom_hline(yintercept = 0, size = 1, colour = "#333333") +
  coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
  ## and the chart labels
  labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
       subtitle = paste0("Data as of ",lastyear), 
       x = " ",
       y = "# of Forcibly displaced people",
       caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
  scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
  geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
  unhcRstyle::unhcr_theme(base_size = 8)  + ## Insert UNHCR Style
  theme(panel.grid.major.x = element_line(color = "#cbcbcb"), 
        panel.grid.major.y = element_blank()) ### changing grid line that should appear

Demographics

demographics1 <- demographics %>%
                   dplyr::left_join( unhcrdatapackage::reference %>% 
                                       select(UNHCRBureau, iso_3),  
                                     by = c("CountryAsylumCode" = "iso_3")) %>% 
  filter(CountryAsylumName  == thiscountry & Year == lastyear-1) %>%
                  mutate ( totGen = FemaleTotal +MaleTotal,
                           totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
                             Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
                           hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))

tot <- format( sum(demographics1$Total) ,  big.mark=",")
totprop <-   format( round( sum(demographics1$totGen) / 
    sum(demographics1$Total )  *100,1),  big.mark=",")  

if (totprop == 0 ) {
  cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
  
} else {
#names(demographics)
pyramid <-  demographics1[ demographics1$Year == max(demographics1$Year),
                          c(
                              "Female04",
                              "Female511",
                              "Female1217",
                              "Female1859",
                              "Female60ormore",
                              "FemaleUnknown",
                             # "FemaleTotal",
                              "Male04",
                              "Male511",
                              "Male1217",
                              "Male1859",
                              "Male60ormore",
                              "MaleUnknown"#,
                             # "MaleTotal"       
                             )]  

pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
  pivot_longer(
    cols = Female04:MaleUnknown,
    names_to = "Class",
    values_to = "Sum",
    values_drop_na = TRUE
  ) 

pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
                                          by = list(pyramid2$Class#, pyramid2$REGION_UN
                                                    ),
                                          sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"

pyramid3 <- pyramid3 %>% 
  mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
                            str_detect(Class, "Female") ~ "Female")) %>% 
  mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
                         str_detect(Class, "511") ~ "5-11",
                         str_detect(Class, "1217") ~ "12-17",
                         str_detect(Class, "1859") ~ "18-59",
                         str_detect(Class, "60") ~ "60+",
                         str_detect(Class, "Unknown") ~ "Unknown")) 

pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11",  "12-17",  "18-59", "60+", "Unknown"))

pyramidplot <- ggplot(pyramid3, aes(x = age, 
                     fill = gender,
                      y = ifelse(test = gender == "Female",
                            yes = -pc, no = pc)) ) + 
  geom_bar(stat = "identity") +
  geom_label(aes(x = age,
                 y = ifelse(test = gender == "Female",
                            yes = -pc -5 , no = pc),
                label =  format(round(pc, 1),  big.mark=",") 
                ),
             hjust = 0,
             vjust = 0.5,
             colour = "black",
             fill = NA,
             label.size = NA,
             family = "Lato",
             size = 8) +
  scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
  labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
       subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
       x = "", 
       y = "Percent of population",
       caption =  "Source: UNHCR Population Statistics as of end 2020") +
  scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
                      aesthetics = c("colour", "fill")) +
  coord_flip()+
  unhcRstyle::unhcr_theme(base_size = 8) +
   theme(panel.grid.major.x = element_line(color = "#cbcbcb"), 
            panel.grid.major.y = element_blank())

pyramidplot
}

Solution

Processing

RSD activities are mostly for ..

Source

#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
data <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long, 
                                                     y= unhcrdatapackage::reference, 
                                                     by = c("CountryAsylumCode" = "iso_3"))  %>%
  filter(CountryOriginName  == thiscountry & Year == lastyear) %>%   
  mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
        CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
        CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>% 
  group_by( CountryAsylumName, SUBREGION) %>%
  summarise(Value2 = sum(Value) )  %>%
  #mutate( value3 =  format_si(Value2)) %>%
  arrange(desc(Value2)) %>%
  head(10)

#Make plot
ggplot(data, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
                           y = Value2)) +
  geom_bar(stat = "identity", 
           position = "identity", 
           fill = "#0072bc") + # here we configure that it will be bar chart+
  geom_label(aes(x = CountryAsylumName, y = Value2, 
                label =  format(round(Value2, -3),  big.mark=",")), 
             hjust = 1, 
             vjust = 0.5, 
             colour = "white", 
             fill = NA, 
             label.size = NA, 
             family = "Lato", 
             size = 6) +
  geom_hline(yintercept = 0, size = 1, colour = "#333333") +
  coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
  ## and the chart labels
  labs(title = paste0("Top 10 Countries hosting Persons from ",thiscountry, " in needs of International Protection" ),
       subtitle = paste0("Data as of ",lastyear), 
       x = "",
       y = "",
       caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
  scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
  geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
  unhcRstyle::unhcr_theme(base_size = 8)  + ## Insert UNHCR Style
  theme(panel.grid.major.x = element_line(color = "#cbcbcb"), 
        panel.grid.major.y = element_blank()) ### changing grid line that should appear

Chile

Categories

Origin

#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
data <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long, 
                                                     y= unhcrdatapackage::reference, 
                                                     by = c("CountryAsylumCode" = "iso_3"))  %>%
  filter(CountryAsylumName  == thiscountry & Year == lastyear) %>%   
  mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
        CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
        CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>% 
  group_by( CountryOriginName, SUBREGION) %>%
  summarise(Value2 = sum(Value) )  %>%
  #mutate( value3 =  format_si(Value2)) %>%
  arrange(desc(Value2)) %>%
  head(10)

#Make plot
ggplot(data, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
                           y = Value2)) +
  geom_bar(stat = "identity", 
           position = "identity", 
           fill = "#0072bc") + # here we configure that it will be bar chart+
  geom_label(aes(x = CountryOriginName, y = Value2, 
                label =  format(round(Value2, -3),  big.mark=",")), 
             hjust = 1, 
             vjust = 0.5, 
             colour = "white", 
             fill = NA, 
             label.size = NA, 
             family = "Lato", 
             size = 6) +
  geom_hline(yintercept = 0, size = 1, colour = "#333333") +
  coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
  ## and the chart labels
  labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
       subtitle = paste0("Data as of ",lastyear), 
       x = " ",
       y = "# of Forcibly displaced people",
       caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
  scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
  geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
  unhcRstyle::unhcr_theme(base_size = 8)  + ## Insert UNHCR Style
  theme(panel.grid.major.x = element_line(color = "#cbcbcb"), 
        panel.grid.major.y = element_blank()) ### changing grid line that should appear

Demographics

demographics1 <- demographics %>%
                   dplyr::left_join( unhcrdatapackage::reference %>% 
                                       select(UNHCRBureau, iso_3),  
                                     by = c("CountryAsylumCode" = "iso_3")) %>% 
  filter(CountryAsylumName  == thiscountry & Year == lastyear-1) %>%
                  mutate ( totGen = FemaleTotal +MaleTotal,
                           totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
                             Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
                           hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))

tot <- format( sum(demographics1$Total) ,  big.mark=",")
totprop <-   format( round( sum(demographics1$totGen) / 
    sum(demographics1$Total )  *100,1),  big.mark=",")  

if (totprop == 0 ) {
  cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
  
} else {
#names(demographics)
pyramid <-  demographics1[ demographics1$Year == max(demographics1$Year),
                          c(
                              "Female04",
                              "Female511",
                              "Female1217",
                              "Female1859",
                              "Female60ormore",
                              "FemaleUnknown",
                             # "FemaleTotal",
                              "Male04",
                              "Male511",
                              "Male1217",
                              "Male1859",
                              "Male60ormore",
                              "MaleUnknown"#,
                             # "MaleTotal"       
                             )]  

pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
  pivot_longer(
    cols = Female04:MaleUnknown,
    names_to = "Class",
    values_to = "Sum",
    values_drop_na = TRUE
  ) 

pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
                                          by = list(pyramid2$Class#, pyramid2$REGION_UN
                                                    ),
                                          sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"

pyramid3 <- pyramid3 %>% 
  mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
                            str_detect(Class, "Female") ~ "Female")) %>% 
  mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
                         str_detect(Class, "511") ~ "5-11",
                         str_detect(Class, "1217") ~ "12-17",
                         str_detect(Class, "1859") ~ "18-59",
                         str_detect(Class, "60") ~ "60+",
                         str_detect(Class, "Unknown") ~ "Unknown")) 

pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11",  "12-17",  "18-59", "60+", "Unknown"))

pyramidplot <- ggplot(pyramid3, aes(x = age, 
                     fill = gender,
                      y = ifelse(test = gender == "Female",
                            yes = -pc, no = pc)) ) + 
  geom_bar(stat = "identity") +
  geom_label(aes(x = age,
                 y = ifelse(test = gender == "Female",
                            yes = -pc -5 , no = pc),
                label =  format(round(pc, 1),  big.mark=",") 
                ),
             hjust = 0,
             vjust = 0.5,
             colour = "black",
             fill = NA,
             label.size = NA,
             family = "Lato",
             size = 8) +
  scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
  labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
       subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
       x = "", 
       y = "Percent of population",
       caption =  "Source: UNHCR Population Statistics as of end 2020") +
  scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
                      aesthetics = c("colour", "fill")) +
  coord_flip()+
  unhcRstyle::unhcr_theme(base_size = 8) +
   theme(panel.grid.major.x = element_line(color = "#cbcbcb"), 
            panel.grid.major.y = element_blank())

pyramidplot
}

Solution

Processing

RSD activities are mostly for ..

Source

#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
data <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long, 
                                                     y= unhcrdatapackage::reference, 
                                                     by = c("CountryAsylumCode" = "iso_3"))  %>%
  filter(CountryOriginName  == thiscountry & Year == lastyear) %>%   
  mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
        CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
        CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>% 
  group_by( CountryAsylumName, SUBREGION) %>%
  summarise(Value2 = sum(Value) )  %>%
  #mutate( value3 =  format_si(Value2)) %>%
  arrange(desc(Value2)) %>%
  head(10)

#Make plot
ggplot(data, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
                           y = Value2)) +
  geom_bar(stat = "identity", 
           position = "identity", 
           fill = "#0072bc") + # here we configure that it will be bar chart+
  geom_label(aes(x = CountryAsylumName, y = Value2, 
                label =  format(round(Value2, -3),  big.mark=",")), 
             hjust = 1, 
             vjust = 0.5, 
             colour = "white", 
             fill = NA, 
             label.size = NA, 
             family = "Lato", 
             size = 6) +
  geom_hline(yintercept = 0, size = 1, colour = "#333333") +
  coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
  ## and the chart labels
  labs(title = paste0("Top 10 Countries hosting Persons from ",thiscountry, " in needs of International Protection" ),
       subtitle = paste0("Data as of ",lastyear), 
       x = "",
       y = "",
       caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
  scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
  geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
  unhcRstyle::unhcr_theme(base_size = 8)  + ## Insert UNHCR Style
  theme(panel.grid.major.x = element_line(color = "#cbcbcb"), 
        panel.grid.major.y = element_blank()) ### changing grid line that should appear

Colombia

Categories

Origin

#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
data <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long, 
                                                     y= unhcrdatapackage::reference, 
                                                     by = c("CountryAsylumCode" = "iso_3"))  %>%
  filter(CountryAsylumName  == thiscountry & Year == lastyear) %>%   
  mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
        CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
        CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>% 
  group_by( CountryOriginName, SUBREGION) %>%
  summarise(Value2 = sum(Value) )  %>%
  #mutate( value3 =  format_si(Value2)) %>%
  arrange(desc(Value2)) %>%
  head(10)

#Make plot
ggplot(data, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
                           y = Value2)) +
  geom_bar(stat = "identity", 
           position = "identity", 
           fill = "#0072bc") + # here we configure that it will be bar chart+
  geom_label(aes(x = CountryOriginName, y = Value2, 
                label =  format(round(Value2, -3),  big.mark=",")), 
             hjust = 1, 
             vjust = 0.5, 
             colour = "white", 
             fill = NA, 
             label.size = NA, 
             family = "Lato", 
             size = 6) +
  geom_hline(yintercept = 0, size = 1, colour = "#333333") +
  coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
  ## and the chart labels
  labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
       subtitle = paste0("Data as of ",lastyear), 
       x = " ",
       y = "# of Forcibly displaced people",
       caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
  scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
  geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
  unhcRstyle::unhcr_theme(base_size = 8)  + ## Insert UNHCR Style
  theme(panel.grid.major.x = element_line(color = "#cbcbcb"), 
        panel.grid.major.y = element_blank()) ### changing grid line that should appear

Demographics

demographics1 <- demographics %>%
                   dplyr::left_join( unhcrdatapackage::reference %>% 
                                       select(UNHCRBureau, iso_3),  
                                     by = c("CountryAsylumCode" = "iso_3")) %>% 
  filter(CountryAsylumName  == thiscountry & Year == lastyear-1) %>%
                  mutate ( totGen = FemaleTotal +MaleTotal,
                           totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
                             Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
                           hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))

tot <- format( sum(demographics1$Total) ,  big.mark=",")
totprop <-   format( round( sum(demographics1$totGen) / 
    sum(demographics1$Total )  *100,1),  big.mark=",")  

if (totprop == 0 ) {
  cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
  
} else {
#names(demographics)
pyramid <-  demographics1[ demographics1$Year == max(demographics1$Year),
                          c(
                              "Female04",
                              "Female511",
                              "Female1217",
                              "Female1859",
                              "Female60ormore",
                              "FemaleUnknown",
                             # "FemaleTotal",
                              "Male04",
                              "Male511",
                              "Male1217",
                              "Male1859",
                              "Male60ormore",
                              "MaleUnknown"#,
                             # "MaleTotal"       
                             )]  

pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
  pivot_longer(
    cols = Female04:MaleUnknown,
    names_to = "Class",
    values_to = "Sum",
    values_drop_na = TRUE
  ) 

pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
                                          by = list(pyramid2$Class#, pyramid2$REGION_UN
                                                    ),
                                          sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"

pyramid3 <- pyramid3 %>% 
  mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
                            str_detect(Class, "Female") ~ "Female")) %>% 
  mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
                         str_detect(Class, "511") ~ "5-11",
                         str_detect(Class, "1217") ~ "12-17",
                         str_detect(Class, "1859") ~ "18-59",
                         str_detect(Class, "60") ~ "60+",
                         str_detect(Class, "Unknown") ~ "Unknown")) 

pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11",  "12-17",  "18-59", "60+", "Unknown"))

pyramidplot <- ggplot(pyramid3, aes(x = age, 
                     fill = gender,
                      y = ifelse(test = gender == "Female",
                            yes = -pc, no = pc)) ) + 
  geom_bar(stat = "identity") +
  geom_label(aes(x = age,
                 y = ifelse(test = gender == "Female",
                            yes = -pc -5 , no = pc),
                label =  format(round(pc, 1),  big.mark=",") 
                ),
             hjust = 0,
             vjust = 0.5,
             colour = "black",
             fill = NA,
             label.size = NA,
             family = "Lato",
             size = 8) +
  scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
  labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
       subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
       x = "", 
       y = "Percent of population",
       caption =  "Source: UNHCR Population Statistics as of end 2020") +
  scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
                      aesthetics = c("colour", "fill")) +
  coord_flip()+
  unhcRstyle::unhcr_theme(base_size = 8) +
   theme(panel.grid.major.x = element_line(color = "#cbcbcb"), 
            panel.grid.major.y = element_blank())

pyramidplot
}

Solution

Processing

RSD activities are mostly for ..

Source

#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
data <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long, 
                                                     y= unhcrdatapackage::reference, 
                                                     by = c("CountryAsylumCode" = "iso_3"))  %>%
  filter(CountryOriginName  == thiscountry & Year == lastyear) %>%   
  mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
        CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
        CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>% 
  group_by( CountryAsylumName, SUBREGION) %>%
  summarise(Value2 = sum(Value) )  %>%
  #mutate( value3 =  format_si(Value2)) %>%
  arrange(desc(Value2)) %>%
  head(10)

#Make plot
ggplot(data, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
                           y = Value2)) +
  geom_bar(stat = "identity", 
           position = "identity", 
           fill = "#0072bc") + # here we configure that it will be bar chart+
  geom_label(aes(x = CountryAsylumName, y = Value2, 
                label =  format(round(Value2, -3),  big.mark=",")), 
             hjust = 1, 
             vjust = 0.5, 
             colour = "white", 
             fill = NA, 
             label.size = NA, 
             family = "Lato", 
             size = 6) +
  geom_hline(yintercept = 0, size = 1, colour = "#333333") +
  coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
  ## and the chart labels
  labs(title = paste0("Top 10 Countries hosting Persons from ",thiscountry, " in needs of International Protection" ),
       subtitle = paste0("Data as of ",lastyear), 
       x = "",
       y = "",
       caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
  scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
  geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
  unhcRstyle::unhcr_theme(base_size = 8)  + ## Insert UNHCR Style
  theme(panel.grid.major.x = element_line(color = "#cbcbcb"), 
        panel.grid.major.y = element_blank()) ### changing grid line that should appear

Ecuador

Categories

Origin

#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
data <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long, 
                                                     y= unhcrdatapackage::reference, 
                                                     by = c("CountryAsylumCode" = "iso_3"))  %>%
  filter(CountryAsylumName  == thiscountry & Year == lastyear) %>%   
  mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
        CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
        CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>% 
  group_by( CountryOriginName, SUBREGION) %>%
  summarise(Value2 = sum(Value) )  %>%
  #mutate( value3 =  format_si(Value2)) %>%
  arrange(desc(Value2)) %>%
  head(10)

#Make plot
ggplot(data, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
                           y = Value2)) +
  geom_bar(stat = "identity", 
           position = "identity", 
           fill = "#0072bc") + # here we configure that it will be bar chart+
  geom_label(aes(x = CountryOriginName, y = Value2, 
                label =  format(round(Value2, -3),  big.mark=",")), 
             hjust = 1, 
             vjust = 0.5, 
             colour = "white", 
             fill = NA, 
             label.size = NA, 
             family = "Lato", 
             size = 6) +
  geom_hline(yintercept = 0, size = 1, colour = "#333333") +
  coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
  ## and the chart labels
  labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
       subtitle = paste0("Data as of ",lastyear), 
       x = " ",
       y = "# of Forcibly displaced people",
       caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
  scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
  geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
  unhcRstyle::unhcr_theme(base_size = 8)  + ## Insert UNHCR Style
  theme(panel.grid.major.x = element_line(color = "#cbcbcb"), 
        panel.grid.major.y = element_blank()) ### changing grid line that should appear

Demographics

demographics1 <- demographics %>%
                   dplyr::left_join( unhcrdatapackage::reference %>% 
                                       select(UNHCRBureau, iso_3),  
                                     by = c("CountryAsylumCode" = "iso_3")) %>% 
  filter(CountryAsylumName  == thiscountry & Year == lastyear-1) %>%
                  mutate ( totGen = FemaleTotal +MaleTotal,
                           totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
                             Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
                           hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))

tot <- format( sum(demographics1$Total) ,  big.mark=",")
totprop <-   format( round( sum(demographics1$totGen) / 
    sum(demographics1$Total )  *100,1),  big.mark=",")  

if (totprop == 0 ) {
  cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
  
} else {
#names(demographics)
pyramid <-  demographics1[ demographics1$Year == max(demographics1$Year),
                          c(
                              "Female04",
                              "Female511",
                              "Female1217",
                              "Female1859",
                              "Female60ormore",
                              "FemaleUnknown",
                             # "FemaleTotal",
                              "Male04",
                              "Male511",
                              "Male1217",
                              "Male1859",
                              "Male60ormore",
                              "MaleUnknown"#,
                             # "MaleTotal"       
                             )]  

pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
  pivot_longer(
    cols = Female04:MaleUnknown,
    names_to = "Class",
    values_to = "Sum",
    values_drop_na = TRUE
  ) 

pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
                                          by = list(pyramid2$Class#, pyramid2$REGION_UN
                                                    ),
                                          sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"

pyramid3 <- pyramid3 %>% 
  mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
                            str_detect(Class, "Female") ~ "Female")) %>% 
  mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
                         str_detect(Class, "511") ~ "5-11",
                         str_detect(Class, "1217") ~ "12-17",
                         str_detect(Class, "1859") ~ "18-59",
                         str_detect(Class, "60") ~ "60+",
                         str_detect(Class, "Unknown") ~ "Unknown")) 

pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11",  "12-17",  "18-59", "60+", "Unknown"))

pyramidplot <- ggplot(pyramid3, aes(x = age, 
                     fill = gender,
                      y = ifelse(test = gender == "Female",
                            yes = -pc, no = pc)) ) + 
  geom_bar(stat = "identity") +
  geom_label(aes(x = age,
                 y = ifelse(test = gender == "Female",
                            yes = -pc -5 , no = pc),
                label =  format(round(pc, 1),  big.mark=",") 
                ),
             hjust = 0,
             vjust = 0.5,
             colour = "black",
             fill = NA,
             label.size = NA,
             family = "Lato",
             size = 8) +
  scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
  labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
       subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
       x = "", 
       y = "Percent of population",
       caption =  "Source: UNHCR Population Statistics as of end 2020") +
  scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
                      aesthetics = c("colour", "fill")) +
  coord_flip()+
  unhcRstyle::unhcr_theme(base_size = 8) +
   theme(panel.grid.major.x = element_line(color = "#cbcbcb"), 
            panel.grid.major.y = element_blank())

pyramidplot
}

Solution

Processing

RSD activities are mostly for ..

Source

#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
data <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long, 
                                                     y= unhcrdatapackage::reference, 
                                                     by = c("CountryAsylumCode" = "iso_3"))  %>%
  filter(CountryOriginName  == thiscountry & Year == lastyear) %>%   
  mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
        CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
        CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>% 
  group_by( CountryAsylumName, SUBREGION) %>%
  summarise(Value2 = sum(Value) )  %>%
  #mutate( value3 =  format_si(Value2)) %>%
  arrange(desc(Value2)) %>%
  head(10)

#Make plot
ggplot(data, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
                           y = Value2)) +
  geom_bar(stat = "identity", 
           position = "identity", 
           fill = "#0072bc") + # here we configure that it will be bar chart+
  geom_label(aes(x = CountryAsylumName, y = Value2, 
                label =  format(round(Value2, -3),  big.mark=",")), 
             hjust = 1, 
             vjust = 0.5, 
             colour = "white", 
             fill = NA, 
             label.size = NA, 
             family = "Lato", 
             size = 6) +
  geom_hline(yintercept = 0, size = 1, colour = "#333333") +
  coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
  ## and the chart labels
  labs(title = paste0("Top 10 Countries hosting Persons from ",thiscountry, " in needs of International Protection" ),
       subtitle = paste0("Data as of ",lastyear), 
       x = "",
       y = "",
       caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
  scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
  geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
  unhcRstyle::unhcr_theme(base_size = 8)  + ## Insert UNHCR Style
  theme(panel.grid.major.x = element_line(color = "#cbcbcb"), 
        panel.grid.major.y = element_blank()) ### changing grid line that should appear

Mexico

Categories

Origin

#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
data <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long, 
                                                     y= unhcrdatapackage::reference, 
                                                     by = c("CountryAsylumCode" = "iso_3"))  %>%
  filter(CountryAsylumName  == thiscountry & Year == lastyear) %>%   
  mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
        CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
        CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>% 
  group_by( CountryOriginName, SUBREGION) %>%
  summarise(Value2 = sum(Value) )  %>%
  #mutate( value3 =  format_si(Value2)) %>%
  arrange(desc(Value2)) %>%
  head(10)

#Make plot
ggplot(data, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
                           y = Value2)) +
  geom_bar(stat = "identity", 
           position = "identity", 
           fill = "#0072bc") + # here we configure that it will be bar chart+
  geom_label(aes(x = CountryOriginName, y = Value2, 
                label =  format(round(Value2, -3),  big.mark=",")), 
             hjust = 1, 
             vjust = 0.5, 
             colour = "white", 
             fill = NA, 
             label.size = NA, 
             family = "Lato", 
             size = 6) +
  geom_hline(yintercept = 0, size = 1, colour = "#333333") +
  coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
  ## and the chart labels
  labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
       subtitle = paste0("Data as of ",lastyear), 
       x = " ",
       y = "# of Forcibly displaced people",
       caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
  scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
  geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
  unhcRstyle::unhcr_theme(base_size = 8)  + ## Insert UNHCR Style
  theme(panel.grid.major.x = element_line(color = "#cbcbcb"), 
        panel.grid.major.y = element_blank()) ### changing grid line that should appear

Demographics

demographics1 <- demographics %>%
                   dplyr::left_join( unhcrdatapackage::reference %>% 
                                       select(UNHCRBureau, iso_3),  
                                     by = c("CountryAsylumCode" = "iso_3")) %>% 
  filter(CountryAsylumName  == thiscountry & Year == lastyear-1) %>%
                  mutate ( totGen = FemaleTotal +MaleTotal,
                           totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
                             Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
                           hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))

tot <- format( sum(demographics1$Total) ,  big.mark=",")
totprop <-   format( round( sum(demographics1$totGen) / 
    sum(demographics1$Total )  *100,1),  big.mark=",")  

if (totprop == 0 ) {
  cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
  
} else {
#names(demographics)
pyramid <-  demographics1[ demographics1$Year == max(demographics1$Year),
                          c(
                              "Female04",
                              "Female511",
                              "Female1217",
                              "Female1859",
                              "Female60ormore",
                              "FemaleUnknown",
                             # "FemaleTotal",
                              "Male04",
                              "Male511",
                              "Male1217",
                              "Male1859",
                              "Male60ormore",
                              "MaleUnknown"#,
                             # "MaleTotal"       
                             )]  

pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
  pivot_longer(
    cols = Female04:MaleUnknown,
    names_to = "Class",
    values_to = "Sum",
    values_drop_na = TRUE
  ) 

pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
                                          by = list(pyramid2$Class#, pyramid2$REGION_UN
                                                    ),
                                          sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"

pyramid3 <- pyramid3 %>% 
  mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
                            str_detect(Class, "Female") ~ "Female")) %>% 
  mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
                         str_detect(Class, "511") ~ "5-11",
                         str_detect(Class, "1217") ~ "12-17",
                         str_detect(Class, "1859") ~ "18-59",
                         str_detect(Class, "60") ~ "60+",
                         str_detect(Class, "Unknown") ~ "Unknown")) 

pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11",  "12-17",  "18-59", "60+", "Unknown"))

pyramidplot <- ggplot(pyramid3, aes(x = age, 
                     fill = gender,
                      y = ifelse(test = gender == "Female",
                            yes = -pc, no = pc)) ) + 
  geom_bar(stat = "identity") +
  geom_label(aes(x = age,
                 y = ifelse(test = gender == "Female",
                            yes = -pc -5 , no = pc),
                label =  format(round(pc, 1),  big.mark=",") 
                ),
             hjust = 0,
             vjust = 0.5,
             colour = "black",
             fill = NA,
             label.size = NA,
             family = "Lato",
             size = 8) +
  scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
  labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
       subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
       x = "", 
       y = "Percent of population",
       caption =  "Source: UNHCR Population Statistics as of end 2020") +
  scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
                      aesthetics = c("colour", "fill")) +
  coord_flip()+
  unhcRstyle::unhcr_theme(base_size = 8) +
   theme(panel.grid.major.x = element_line(color = "#cbcbcb"), 
            panel.grid.major.y = element_blank())

pyramidplot
}

Solution

Processing

RSD activities are mostly for ..

Source

#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
data <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long, 
                                                     y= unhcrdatapackage::reference, 
                                                     by = c("CountryAsylumCode" = "iso_3"))  %>%
  filter(CountryOriginName  == thiscountry & Year == lastyear) %>%   
  mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
        CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
        CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>% 
  group_by( CountryAsylumName, SUBREGION) %>%
  summarise(Value2 = sum(Value) )  %>%
  #mutate( value3 =  format_si(Value2)) %>%
  arrange(desc(Value2)) %>%
  head(10)

#Make plot
ggplot(data, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
                           y = Value2)) +
  geom_bar(stat = "identity", 
           position = "identity", 
           fill = "#0072bc") + # here we configure that it will be bar chart+
  geom_label(aes(x = CountryAsylumName, y = Value2, 
                label =  format(round(Value2, -3),  big.mark=",")), 
             hjust = 1, 
             vjust = 0.5, 
             colour = "white", 
             fill = NA, 
             label.size = NA, 
             family = "Lato", 
             size = 6) +
  geom_hline(yintercept = 0, size = 1, colour = "#333333") +
  coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
  ## and the chart labels
  labs(title = paste0("Top 10 Countries hosting Persons from ",thiscountry, " in needs of International Protection" ),
       subtitle = paste0("Data as of ",lastyear), 
       x = "",
       y = "",
       caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
  scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
  geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
  unhcRstyle::unhcr_theme(base_size = 8)  + ## Insert UNHCR Style
  theme(panel.grid.major.x = element_line(color = "#cbcbcb"), 
        panel.grid.major.y = element_blank()) ### changing grid line that should appear

Peru

Categories

Origin

#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
data <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long, 
                                                     y= unhcrdatapackage::reference, 
                                                     by = c("CountryAsylumCode" = "iso_3"))  %>%
  filter(CountryAsylumName  == thiscountry & Year == lastyear) %>%   
  mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
        CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
        CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>% 
  group_by( CountryOriginName, SUBREGION) %>%
  summarise(Value2 = sum(Value) )  %>%
  #mutate( value3 =  format_si(Value2)) %>%
  arrange(desc(Value2)) %>%
  head(10)

#Make plot
ggplot(data, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
                           y = Value2)) +
  geom_bar(stat = "identity", 
           position = "identity", 
           fill = "#0072bc") + # here we configure that it will be bar chart+
  geom_label(aes(x = CountryOriginName, y = Value2, 
                label =  format(round(Value2, -3),  big.mark=",")), 
             hjust = 1, 
             vjust = 0.5, 
             colour = "white", 
             fill = NA, 
             label.size = NA, 
             family = "Lato", 
             size = 6) +
  geom_hline(yintercept = 0, size = 1, colour = "#333333") +
  coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
  ## and the chart labels
  labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
       subtitle = paste0("Data as of ",lastyear), 
       x = " ",
       y = "# of Forcibly displaced people",
       caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
  scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
  geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
  unhcRstyle::unhcr_theme(base_size = 8)  + ## Insert UNHCR Style
  theme(panel.grid.major.x = element_line(color = "#cbcbcb"), 
        panel.grid.major.y = element_blank()) ### changing grid line that should appear

Demographics

demographics1 <- demographics %>%
                   dplyr::left_join( unhcrdatapackage::reference %>% 
                                       select(UNHCRBureau, iso_3),  
                                     by = c("CountryAsylumCode" = "iso_3")) %>% 
  filter(CountryAsylumName  == thiscountry & Year == lastyear-1) %>%
                  mutate ( totGen = FemaleTotal +MaleTotal,
                           totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
                             Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
                           hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))

tot <- format( sum(demographics1$Total) ,  big.mark=",")
totprop <-   format( round( sum(demographics1$totGen) / 
    sum(demographics1$Total )  *100,1),  big.mark=",")  

if (totprop == 0 ) {
  cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
  
} else {
#names(demographics)
pyramid <-  demographics1[ demographics1$Year == max(demographics1$Year),
                          c(
                              "Female04",
                              "Female511",
                              "Female1217",
                              "Female1859",
                              "Female60ormore",
                              "FemaleUnknown",
                             # "FemaleTotal",
                              "Male04",
                              "Male511",
                              "Male1217",
                              "Male1859",
                              "Male60ormore",
                              "MaleUnknown"#,
                             # "MaleTotal"       
                             )]  

pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
  pivot_longer(
    cols = Female04:MaleUnknown,
    names_to = "Class",
    values_to = "Sum",
    values_drop_na = TRUE
  ) 

pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
                                          by = list(pyramid2$Class#, pyramid2$REGION_UN
                                                    ),
                                          sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"

pyramid3 <- pyramid3 %>% 
  mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
                            str_detect(Class, "Female") ~ "Female")) %>% 
  mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
                         str_detect(Class, "511") ~ "5-11",
                         str_detect(Class, "1217") ~ "12-17",
                         str_detect(Class, "1859") ~ "18-59",
                         str_detect(Class, "60") ~ "60+",
                         str_detect(Class, "Unknown") ~ "Unknown")) 

pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11",  "12-17",  "18-59", "60+", "Unknown"))

pyramidplot <- ggplot(pyramid3, aes(x = age, 
                     fill = gender,
                      y = ifelse(test = gender == "Female",
                            yes = -pc, no = pc)) ) + 
  geom_bar(stat = "identity") +
  geom_label(aes(x = age,
                 y = ifelse(test = gender == "Female",
                            yes = -pc -5 , no = pc),
                label =  format(round(pc, 1),  big.mark=",") 
                ),
             hjust = 0,
             vjust = 0.5,
             colour = "black",
             fill = NA,
             label.size = NA,
             family = "Lato",
             size = 8) +
  scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
  labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
       subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
       x = "", 
       y = "Percent of population",
       caption =  "Source: UNHCR Population Statistics as of end 2020") +
  scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
                      aesthetics = c("colour", "fill")) +
  coord_flip()+
  unhcRstyle::unhcr_theme(base_size = 8) +
   theme(panel.grid.major.x = element_line(color = "#cbcbcb"), 
            panel.grid.major.y = element_blank())

pyramidplot
}

#>  Gender disaggregation is not available for all of the 1,557,216 persons inPeru

Solution

Processing

RSD activities are mostly for ..

Source

#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
data <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long, 
                                                     y= unhcrdatapackage::reference, 
                                                     by = c("CountryAsylumCode" = "iso_3"))  %>%
  filter(CountryOriginName  == thiscountry & Year == lastyear) %>%   
  mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
        CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
        CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>% 
  group_by( CountryAsylumName, SUBREGION) %>%
  summarise(Value2 = sum(Value) )  %>%
  #mutate( value3 =  format_si(Value2)) %>%
  arrange(desc(Value2)) %>%
  head(10)

#Make plot
ggplot(data, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
                           y = Value2)) +
  geom_bar(stat = "identity", 
           position = "identity", 
           fill = "#0072bc") + # here we configure that it will be bar chart+
  geom_label(aes(x = CountryAsylumName, y = Value2, 
                label =  format(round(Value2, -3),  big.mark=",")), 
             hjust = 1, 
             vjust = 0.5, 
             colour = "white", 
             fill = NA, 
             label.size = NA, 
             family = "Lato", 
             size = 6) +
  geom_hline(yintercept = 0, size = 1, colour = "#333333") +
  coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
  ## and the chart labels
  labs(title = paste0("Top 10 Countries hosting Persons from ",thiscountry, " in needs of International Protection" ),
       subtitle = paste0("Data as of ",lastyear), 
       x = "",
       y = "",
       caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
  scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
  geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
  unhcRstyle::unhcr_theme(base_size = 8)  + ## Insert UNHCR Style
  theme(panel.grid.major.x = element_line(color = "#cbcbcb"), 
        panel.grid.major.y = element_blank()) ### changing grid line that should appear

USA

Categories

Origin

#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
data <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long, 
                                                     y= unhcrdatapackage::reference, 
                                                     by = c("CountryAsylumCode" = "iso_3"))  %>%
  filter(CountryAsylumName  == thiscountry & Year == lastyear) %>%   
  mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
        CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
        CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>% 
  group_by( CountryOriginName, SUBREGION) %>%
  summarise(Value2 = sum(Value) )  %>%
  #mutate( value3 =  format_si(Value2)) %>%
  arrange(desc(Value2)) %>%
  head(10)

#Make plot
ggplot(data, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
                           y = Value2)) +
  geom_bar(stat = "identity", 
           position = "identity", 
           fill = "#0072bc") + # here we configure that it will be bar chart+
  geom_label(aes(x = CountryOriginName, y = Value2, 
                label =  format(round(Value2, -3),  big.mark=",")), 
             hjust = 1, 
             vjust = 0.5, 
             colour = "white", 
             fill = NA, 
             label.size = NA, 
             family = "Lato", 
             size = 6) +
  geom_hline(yintercept = 0, size = 1, colour = "#333333") +
  coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
  ## and the chart labels
  labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
       subtitle = paste0("Data as of ",lastyear), 
       x = " ",
       y = "# of Forcibly displaced people",
       caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
  scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
  geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
  unhcRstyle::unhcr_theme(base_size = 8)  + ## Insert UNHCR Style
  theme(panel.grid.major.x = element_line(color = "#cbcbcb"), 
        panel.grid.major.y = element_blank()) ### changing grid line that should appear

Demographics

demographics1 <- demographics %>%
                   dplyr::left_join( unhcrdatapackage::reference %>% 
                                       select(UNHCRBureau, iso_3),  
                                     by = c("CountryAsylumCode" = "iso_3")) %>% 
  filter(CountryAsylumName  == thiscountry & Year == lastyear-1) %>%
                  mutate ( totGen = FemaleTotal +MaleTotal,
                           totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
                             Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
                           hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))

tot <- format( sum(demographics1$Total) ,  big.mark=",")
totprop <-   format( round( sum(demographics1$totGen) / 
    sum(demographics1$Total )  *100,1),  big.mark=",")  

if (totprop == 0 ) {
  cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
  
} else {
#names(demographics)
pyramid <-  demographics1[ demographics1$Year == max(demographics1$Year),
                          c(
                              "Female04",
                              "Female511",
                              "Female1217",
                              "Female1859",
                              "Female60ormore",
                              "FemaleUnknown",
                             # "FemaleTotal",
                              "Male04",
                              "Male511",
                              "Male1217",
                              "Male1859",
                              "Male60ormore",
                              "MaleUnknown"#,
                             # "MaleTotal"       
                             )]  

pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
  pivot_longer(
    cols = Female04:MaleUnknown,
    names_to = "Class",
    values_to = "Sum",
    values_drop_na = TRUE
  ) 

pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
                                          by = list(pyramid2$Class#, pyramid2$REGION_UN
                                                    ),
                                          sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"

pyramid3 <- pyramid3 %>% 
  mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
                            str_detect(Class, "Female") ~ "Female")) %>% 
  mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
                         str_detect(Class, "511") ~ "5-11",
                         str_detect(Class, "1217") ~ "12-17",
                         str_detect(Class, "1859") ~ "18-59",
                         str_detect(Class, "60") ~ "60+",
                         str_detect(Class, "Unknown") ~ "Unknown")) 

pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11",  "12-17",  "18-59", "60+", "Unknown"))

pyramidplot <- ggplot(pyramid3, aes(x = age, 
                     fill = gender,
                      y = ifelse(test = gender == "Female",
                            yes = -pc, no = pc)) ) + 
  geom_bar(stat = "identity") +
  geom_label(aes(x = age,
                 y = ifelse(test = gender == "Female",
                            yes = -pc -5 , no = pc),
                label =  format(round(pc, 1),  big.mark=",") 
                ),
             hjust = 0,
             vjust = 0.5,
             colour = "black",
             fill = NA,
             label.size = NA,
             family = "Lato",
             size = 8) +
  scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
  labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
       subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
       x = "", 
       y = "Percent of population",
       caption =  "Source: UNHCR Population Statistics as of end 2020") +
  scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
                      aesthetics = c("colour", "fill")) +
  coord_flip()+
  unhcRstyle::unhcr_theme(base_size = 8) +
   theme(panel.grid.major.x = element_line(color = "#cbcbcb"), 
            panel.grid.major.y = element_blank())

pyramidplot
}

#>  Gender disaggregation is not available for all of the 1,338,835 persons inUnited States of America

Solution

Processing

RSD activities are mostly for ..

Source

#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
data <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long, 
                                                     y= unhcrdatapackage::reference, 
                                                     by = c("CountryAsylumCode" = "iso_3"))  %>%
  filter(CountryOriginName  == thiscountry & Year == lastyear) %>%   
  mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
        CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
        CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>% 
  group_by( CountryAsylumName, SUBREGION) %>%
  summarise(Value2 = sum(Value) )  %>%
  #mutate( value3 =  format_si(Value2)) %>%
  arrange(desc(Value2)) %>%
  head(10)

#Make plot
ggplot(data, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
                           y = Value2)) +
  geom_bar(stat = "identity", 
           position = "identity", 
           fill = "#0072bc") + # here we configure that it will be bar chart+
  geom_label(aes(x = CountryAsylumName, y = Value2, 
                label =  format(round(Value2, -3),  big.mark=",")), 
             hjust = 1, 
             vjust = 0.5, 
             colour = "white", 
             fill = NA, 
             label.size = NA, 
             family = "Lato", 
             size = 6) +
  geom_hline(yintercept = 0, size = 1, colour = "#333333") +
  coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
  ## and the chart labels
  labs(title = paste0("Top 10 Countries hosting Persons from ",thiscountry, " in needs of International Protection" ),
       subtitle = paste0("Data as of ",lastyear), 
       x = "",
       y = "",
       caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
  scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
  geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
  unhcRstyle::unhcr_theme(base_size = 8)  + ## Insert UNHCR Style
  theme(panel.grid.major.x = element_line(color = "#cbcbcb"), 
        panel.grid.major.y = element_blank()) ### changing grid line that should appear

Venezuela

Categories

Origin

#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
data <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long, 
                                                     y= unhcrdatapackage::reference, 
                                                     by = c("CountryAsylumCode" = "iso_3"))  %>%
  filter(CountryAsylumName  == thiscountry & Year == lastyear) %>%   
  mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
        CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
        CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>% 
  group_by( CountryOriginName, SUBREGION) %>%
  summarise(Value2 = sum(Value) )  %>%
  #mutate( value3 =  format_si(Value2)) %>%
  arrange(desc(Value2)) %>%
  head(10)

#Make plot
ggplot(data, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
                           y = Value2)) +
  geom_bar(stat = "identity", 
           position = "identity", 
           fill = "#0072bc") + # here we configure that it will be bar chart+
  geom_label(aes(x = CountryOriginName, y = Value2, 
                label =  format(round(Value2, -3),  big.mark=",")), 
             hjust = 1, 
             vjust = 0.5, 
             colour = "white", 
             fill = NA, 
             label.size = NA, 
             family = "Lato", 
             size = 6) +
  geom_hline(yintercept = 0, size = 1, colour = "#333333") +
  coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
  ## and the chart labels
  labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
       subtitle = paste0("Data as of ",lastyear), 
       x = " ",
       y = "# of Forcibly displaced people",
       caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
  scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
  geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
  unhcRstyle::unhcr_theme(base_size = 8)  + ## Insert UNHCR Style
  theme(panel.grid.major.x = element_line(color = "#cbcbcb"), 
        panel.grid.major.y = element_blank()) ### changing grid line that should appear

Demographics

demographics1 <- demographics %>%
                   dplyr::left_join( unhcrdatapackage::reference %>% 
                                       select(UNHCRBureau, iso_3),  
                                     by = c("CountryAsylumCode" = "iso_3")) %>% 
  filter(CountryAsylumName  == thiscountry & Year == lastyear-1) %>%
                  mutate ( totGen = FemaleTotal +MaleTotal,
                           totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
                             Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
                           hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))

tot <- format( sum(demographics1$Total) ,  big.mark=",")
totprop <-   format( round( sum(demographics1$totGen) / 
    sum(demographics1$Total )  *100,1),  big.mark=",")  

if (totprop == 0 ) {
  cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
  
} else {
#names(demographics)
pyramid <-  demographics1[ demographics1$Year == max(demographics1$Year),
                          c(
                              "Female04",
                              "Female511",
                              "Female1217",
                              "Female1859",
                              "Female60ormore",
                              "FemaleUnknown",
                             # "FemaleTotal",
                              "Male04",
                              "Male511",
                              "Male1217",
                              "Male1859",
                              "Male60ormore",
                              "MaleUnknown"#,
                             # "MaleTotal"       
                             )]  

pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
  pivot_longer(
    cols = Female04:MaleUnknown,
    names_to = "Class",
    values_to = "Sum",
    values_drop_na = TRUE
  ) 

pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
                                          by = list(pyramid2$Class#, pyramid2$REGION_UN
                                                    ),
                                          sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"

pyramid3 <- pyramid3 %>% 
  mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
                            str_detect(Class, "Female") ~ "Female")) %>% 
  mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
                         str_detect(Class, "511") ~ "5-11",
                         str_detect(Class, "1217") ~ "12-17",
                         str_detect(Class, "1859") ~ "18-59",
                         str_detect(Class, "60") ~ "60+",
                         str_detect(Class, "Unknown") ~ "Unknown")) 

pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11",  "12-17",  "18-59", "60+", "Unknown"))

pyramidplot <- ggplot(pyramid3, aes(x = age, 
                     fill = gender,
                      y = ifelse(test = gender == "Female",
                            yes = -pc, no = pc)) ) + 
  geom_bar(stat = "identity") +
  geom_label(aes(x = age,
                 y = ifelse(test = gender == "Female",
                            yes = -pc -5 , no = pc),
                label =  format(round(pc, 1),  big.mark=",") 
                ),
             hjust = 0,
             vjust = 0.5,
             colour = "black",
             fill = NA,
             label.size = NA,
             family = "Lato",
             size = 8) +
  scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
  labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
       subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
       x = "", 
       y = "Percent of population",
       caption =  "Source: UNHCR Population Statistics as of end 2020") +
  scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
                      aesthetics = c("colour", "fill")) +
  coord_flip()+
  unhcRstyle::unhcr_theme(base_size = 8) +
   theme(panel.grid.major.x = element_line(color = "#cbcbcb"), 
            panel.grid.major.y = element_blank())

pyramidplot
}

Solution

Processing

RSD activities are mostly for ..

Source

#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
data <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long, 
                                                     y= unhcrdatapackage::reference, 
                                                     by = c("CountryAsylumCode" = "iso_3"))  %>%
  filter(CountryOriginName  == thiscountry & Year == lastyear) %>%   
  mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
        CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
        CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>% 
  group_by( CountryAsylumName, SUBREGION) %>%
  summarise(Value2 = sum(Value) )  %>%
  #mutate( value3 =  format_si(Value2)) %>%
  arrange(desc(Value2)) %>%
  head(10)

#Make plot
ggplot(data, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
                           y = Value2)) +
  geom_bar(stat = "identity", 
           position = "identity", 
           fill = "#0072bc") + # here we configure that it will be bar chart+
  geom_label(aes(x = CountryAsylumName, y = Value2, 
                label =  format(round(Value2, -3),  big.mark=",")), 
             hjust = 1, 
             vjust = 0.5, 
             colour = "white", 
             fill = NA, 
             label.size = NA, 
             family = "Lato", 
             size = 6) +
  geom_hline(yintercept = 0, size = 1, colour = "#333333") +
  coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
  ## and the chart labels
  labs(title = paste0("Top 10 Countries hosting Persons from ",thiscountry, " in needs of International Protection" ),
       subtitle = paste0("Data as of ",lastyear), 
       x = "",
       y = "",
       caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
  scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
  geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
  unhcRstyle::unhcr_theme(base_size = 8)  + ## Insert UNHCR Style
  theme(panel.grid.major.x = element_line(color = "#cbcbcb"), 
        panel.grid.major.y = element_blank()) ### changing grid line that should appear

Panama

Categories

Origin

#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
data <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long, 
                                                     y= unhcrdatapackage::reference, 
                                                     by = c("CountryAsylumCode" = "iso_3"))  %>%
  filter(CountryAsylumName  == thiscountry & Year == lastyear) %>%   
  mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
        CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
        CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>% 
  group_by( CountryOriginName, SUBREGION) %>%
  summarise(Value2 = sum(Value) )  %>%
  #mutate( value3 =  format_si(Value2)) %>%
  arrange(desc(Value2)) %>%
  head(10)

#Make plot
ggplot(data, aes(x = reorder(CountryOriginName, Value2), ## Reordering country by Value
                           y = Value2)) +
  geom_bar(stat = "identity", 
           position = "identity", 
           fill = "#0072bc") + # here we configure that it will be bar chart+
  geom_label(aes(x = CountryOriginName, y = Value2, 
                label =  format(round(Value2, -3),  big.mark=",")), 
             hjust = 1, 
             vjust = 0.5, 
             colour = "white", 
             fill = NA, 
             label.size = NA, 
             family = "Lato", 
             size = 6) +
  geom_hline(yintercept = 0, size = 1, colour = "#333333") +
  coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
  ## and the chart labels
  labs(title = paste0("Top 10 Countries of Origin hosted in ",thiscountry, "" ),
       subtitle = paste0("Data as of ",lastyear), 
       x = " ",
       y = "# of Forcibly displaced people",
       caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
  scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
  geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
  unhcRstyle::unhcr_theme(base_size = 8)  + ## Insert UNHCR Style
  theme(panel.grid.major.x = element_line(color = "#cbcbcb"), 
        panel.grid.major.y = element_blank()) ### changing grid line that should appear

Demographics

demographics1 <- demographics %>%
                   dplyr::left_join( unhcrdatapackage::reference %>% 
                                       select(UNHCRBureau, iso_3),  
                                     by = c("CountryAsylumCode" = "iso_3")) %>% 
  filter(CountryAsylumName  == thiscountry & Year == lastyear-1) %>%
                  mutate ( totGen = FemaleTotal +MaleTotal,
                           totbreak = Female04 + Female511 + Female1217 + Female1859 + Female60ormore + FemaleUnknown +
                             Male04 + Male511 + Male1217 + Male1859 + Male60ormore + MaleUnknown,
                           hasbreak = ifelse(Total - totGen == 0, "yes", "no" ))

tot <- format( sum(demographics1$Total) ,  big.mark=",")
totprop <-   format( round( sum(demographics1$totGen) / 
    sum(demographics1$Total )  *100,1),  big.mark=",")  

if (totprop == 0 ) {
  cat(paste0(" Gender disaggregation is not available for all of the ",tot, " persons in", thiscountry ))
  
} else {
#names(demographics)
pyramid <-  demographics1[ demographics1$Year == max(demographics1$Year),
                          c(
                              "Female04",
                              "Female511",
                              "Female1217",
                              "Female1859",
                              "Female60ormore",
                              "FemaleUnknown",
                             # "FemaleTotal",
                              "Male04",
                              "Male511",
                              "Male1217",
                              "Male1859",
                              "Male60ormore",
                              "MaleUnknown"#,
                             # "MaleTotal"       
                             )]  

pyramid2 <- data.frame(lapply(pyramid, function(x) { as.numeric( gsub("NA", "0", x)) })) %>%
  pivot_longer(
    cols = Female04:MaleUnknown,
    names_to = "Class",
    values_to = "Sum",
    values_drop_na = TRUE
  ) 

pyramid3 <- as.data.frame(aggregate(pyramid2$Sum,
                                          by = list(pyramid2$Class#, pyramid2$REGION_UN
                                                    ),
                                          sum))
names(pyramid3)[1] <- "Class"
names(pyramid3)[2] <- "Count"

pyramid3 <- pyramid3 %>% 
  mutate(gender = case_when(str_detect(Class, "Male") ~ "Male",
                            str_detect(Class, "Female") ~ "Female")) %>% 
  mutate(age = case_when(str_detect(Class, "04") ~ "0-4",
                         str_detect(Class, "511") ~ "5-11",
                         str_detect(Class, "1217") ~ "12-17",
                         str_detect(Class, "1859") ~ "18-59",
                         str_detect(Class, "60") ~ "60+",
                         str_detect(Class, "Unknown") ~ "Unknown")) 

pyramid3$pc <- pyramid3$Count / sum(pyramid3$Count) * 100
pyramid3$age <- factor(pyramid3$age, levels = c("0-4", "5-11",  "12-17",  "18-59", "60+", "Unknown"))

pyramidplot <- ggplot(pyramid3, aes(x = age, 
                     fill = gender,
                      y = ifelse(test = gender == "Female",
                            yes = -pc, no = pc)) ) + 
  geom_bar(stat = "identity") +
  geom_label(aes(x = age,
                 y = ifelse(test = gender == "Female",
                            yes = -pc -5 , no = pc),
                label =  format(round(pc, 1),  big.mark=",") 
                ),
             hjust = 0,
             vjust = 0.5,
             colour = "black",
             fill = NA,
             label.size = NA,
             family = "Lato",
             size = 8) +
  scale_y_continuous(labels = abs, limits = max(pyramid3$pc) * c(-1,1)) +
  labs(title = paste0("Population Pyramid for Forcibly Displaced People in", thiscountry ),
       subtitle = paste0(" Gender disaggregation is available for ", totprop, " % of the ",tot, " persons in this country" ),
       x = "", 
       y = "Percent of population",
       caption =  "Source: UNHCR Population Statistics as of end 2020") +
  scale_colour_manual(values = c("#126db4","#01ab91"), # based on Asia Report
                      aesthetics = c("colour", "fill")) +
  coord_flip()+
  unhcRstyle::unhcr_theme(base_size = 8) +
   theme(panel.grid.major.x = element_line(color = "#cbcbcb"), 
            panel.grid.major.y = element_blank())

pyramidplot
}

#>  Gender disaggregation is not available for all of the 134,391 persons inPanama

Solution

Processing

RSD activities are mostly for ..

Source

#Prepare data2
lastyear <- max(unhcrdatapackage::end_year_population_totals_long$Year)
## Loading the stat tables
data <- dplyr::left_join( x= unhcrdatapackage::end_year_population_totals_long, 
                                                     y= unhcrdatapackage::reference, 
                                                     by = c("CountryAsylumCode" = "iso_3"))  %>%
  filter(CountryOriginName  == thiscountry & Year == lastyear) %>%   
  mutate(CountryAsylumName = str_replace(CountryAsylumName, " \\(Bolivarian Republic of\\)", ""),
        CountryAsylumName = str_replace(CountryAsylumName, "Iran \\(Islamic Republic of\\)", "Iran"),
        CountryAsylumName = str_replace(CountryAsylumName, "United Kingdom of Great Britain and Northern Ireland", "UK")) %>% 
  group_by( CountryAsylumName, SUBREGION) %>%
  summarise(Value2 = sum(Value) )  %>%
  #mutate( value3 =  format_si(Value2)) %>%
  arrange(desc(Value2)) %>%
  head(10)

#Make plot
ggplot(data, aes(x = reorder(CountryAsylumName, Value2), ## Reordering country by Value
                           y = Value2)) +
  geom_bar(stat = "identity", 
           position = "identity", 
           fill = "#0072bc") + # here we configure that it will be bar chart+
  geom_label(aes(x = CountryAsylumName, y = Value2, 
                label =  format(round(Value2, -3),  big.mark=",")), 
             hjust = 1, 
             vjust = 0.5, 
             colour = "white", 
             fill = NA, 
             label.size = NA, 
             family = "Lato", 
             size = 6) +
  geom_hline(yintercept = 0, size = 1, colour = "#333333") +
  coord_flip() + # Add `coord_flip()` to make your vertical bars horizontal:
  ## and the chart labels
  labs(title = paste0("Top 10 Countries hosting Persons from ",thiscountry, " in needs of International Protection" ),
       subtitle = paste0("Data as of ",lastyear), 
       x = "",
       y = "",
       caption = "UNHCR https://www.unhcr.org/refugee-statistics/") +
  scale_y_continuous( label = unhcRstyle::format_si()) + ## Format axis number
  geom_hline(yintercept = 0, size = 1.1, colour = "#333333") +
  unhcRstyle::unhcr_theme(base_size = 8)  + ## Insert UNHCR Style
  theme(panel.grid.major.x = element_line(color = "#cbcbcb"), 
        panel.grid.major.y = element_blank()) ### changing grid line that should appear

Asia

To come soon…

Europe

To come soon…

MENA

To come soon…

Western Africa

To come soon…

Eastern Africa

To come soon…

Southern Africa

To come soon…

 

---

UnhcrDataPackage

Re-usable/adjustable Source codes for Country Focus are available Here. Feel free to share comments through issues ticket